Dataset Information

Variation in DNA methylation of human blood over a 1-year period using the Illumina MethylationEPIC array.

ABSTRACT: Assessing DNA methylation profiles in human blood has become a major focus of epidemiologic inquiry. Understanding variability in CpG-specific DNA methylation over moderate periods of time is a critical first step in identifying CpG sites that are candidates for DNA methylation-based etiologic, diagnostic and prognostic predictors of pathogenesis. Using the Illumina MethylationEPIC [850K] BeadArray, DNA methylation was profiled in paired whole blood samples collected approximately 1 year apart from 35 healthy women enrolled in the Nurses Study II cohort. The median intraclass correlation coefficient (ICC) across all CpG loci was 0.19 [Interquartile Range (IQR) 0.00-0.50]; 74.8% of ICCs were in the low range (0-0.5), 16.9% in the mid-range of ICCs (0.5-0.8), and 8.3% in the high-range of ICCs (0.8-1). ICCs were similar for CpG probes on the 450K Illumina array (median 0.17) and the new probes added to the 850K array (median 0.21). ICCs for CpG loci on the sex chromosomes and known metastable epialleles were high (median 0.71, 0.97, respectively), and ICCs among methylation quantitative trait loci (mQTL) CpGs were significantly higher as compared to non-mQTL CpGs (median 0.73, 0.16, respectively, P < 2 × 10^-¹⁶). We observed wide variation in DNA methylation stability over a 1-year period. Probes considered non-stable, due to substantial variation over a moderate period of time and with minimal variability across individuals could be removed in large epidemiological studies. Moreover, adjusting for technical variation that arises from using high-dimensional arrays is critical.

SUBMITTER: Zaimi I

PROVIDER: S-EPMC6342169 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Variation in DNA methylation of human blood over a 1-year period using the Illumina MethylationEPIC array.

Zaimi Ina I Pei Dong D Koestler Devin C DC Marsit Carmen J CJ De Vivo Immaculata I Tworoger Shelley S SS Shields Alexandra E AE Kelsey Karl T KT Michaud Dominique S DS

Epigenetics 20181021 10-11

Assessing DNA methylation profiles in human blood has become a major focus of epidemiologic inquiry. Understanding variability in CpG-specific DNA methylation over moderate periods of time is a critical first step in identifying CpG sites that are candidates for DNA methylation-based etiologic, diagnostic and prognostic predictors of pathogenesis. Using the Illumina MethylationEPIC [850K] BeadArray, DNA methylation was profiled in paired whole blood samples collected approximately 1 year apart f ...[more]

PMID: 30270718

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Project description:The Illumina Infinium MethylationEPIC provides an efficient platform for profiling DNA methylation in humans at over 850,000 CpGs. Model organisms such as mice do not currently benefit from an equivalent array. Here we used this array to measure DNA methylation in mice. We defined probes targeting conserved regions and performed differential methylation analysis and compared between the array-based assay and affinity-based DNA sequencing of methyl-CpGs (MBD-seq) and reduced representation bisulfite sequencing. Mouse samples consisted of 11 liver DNA from two strains, C57BL/6J (B6) and DBA/2J (D2), that varied widely in age. Linear regression was applied to detect differential methylation. In total, 13,665 probes (1.6% of total probes) aligned to conserved CpGs. Beta-values (?-value) for these probes showed a distribution similar to that in humans. Overall, there was high concordance in methylation signal between the EPIC array and MBD-seq (Pearson correlation r = 0.70, p-value < 0.0001). However, the EPIC probes had higher quantitative sensitivity at CpGs that are hypo- (?-value < 0.3) or hypermethylated (?-value > 0.7). In terms of differential methylation, no EPIC probe detected a significant difference between age groups at a Benjamini-Hochberg threshold of 10%, and the MBD-seq performed better at detecting age-dependent change in methylation. However, the top most significant probe for age (cg13269407; uncorrected p-value = 1.8 x 10-5) is part of the clock CpGs used to estimate the human epigenetic age. For strain, 219 EPIC probes detected significant differential methylation (FDR cutoff 10%) with ~80% CpGs associated with higher methylation in D2. This higher methylation profile in D2 compared to B6 was also replicated by the MBD-seq data. To summarize, we found only a small subset of EPIC probes that target conserved sites. However, for this small subset the array provides a reliable assay of DNA methylation and can be effectively used to measure differential methylation in mice.

Project description:BACKGROUND:Epigenome-wide association studies (EWAS) have been widely applied to identify methylation CpG sites associated with human disease. To date, the Infinium MethylationEPIC array (EPIC) is commonly used for high-throughput DNA methylation profiling. However, the EPIC array covers only 30% of the human methylome. Methylation Capture bisulfite sequencing (MC-seq) captures target regions of methylome and has advantages of extensive coverage in the methylome at an affordable price. METHODS:Epigenome-wide DNA methylation in four peripheral blood mononuclear cell samples was profiled by using SureSelectXT Methyl-Seq for MC-seq and EPIC platforms separately. CpG site-based reproducibility of MC-seq was assessed with DNA sample inputs ranging in quantity of high (>?1000 ng), medium (300-1000 ng), and low (150 ng-300 ng). To compare the performance of MC-seq and the EPIC arrays, we conducted a Pearson correlation and methylation value difference at each CpG site that was detected by both MC-seq and EPIC. We compared the percentage and counts in each CpG island and gene annotation between MC-seq and the EPIC array. RESULTS:After quality control, an average of 3,708,550 CpG sites per sample were detected by MC-seq with DNA quantity?>?1000 ng. Reproducibility of DNA methylation in MC-seq-detected CpG sites was high among samples with high, medium, and low DNA inputs (r?>?0.96). The EPIC array captured an average of 846,464 CpG sites per sample. Compared with the EPIC array, MC-seq detected more CpGs in coding regions and CpG islands. Among the 472,540 CpG sites captured by both platforms, methylation of a majority of CpG sites was highly correlated in the same sample (r: 0.98-0.99). However, methylation for a small proportion of CpGs (N?=?235) differed significantly between the two platforms, with differences in beta values of greater than 0.5. CONCLUSIONS:Our results show that MC-seq is an efficient and reliable platform for methylome profiling with a broader coverage of the methylome than the array-based platform. Although methylation measurements in majority of CpGs are highly correlated, a number of CpG sites show large discrepancy between the two platforms, which warrants further investigation and needs cautious interpretation.

Project description:BackgroundCurrent array-based methods for the measurement of DNA methylation rely on the process of sodium bisulfite conversion to differentiate between methylated and unmethylated cytosine bases in DNA. In the absence of genotype data this process can lead to ambiguity in data interpretation when a sample has polymorphisms at a methylation probe site. A common way to minimize this problem is to exclude such potentially problematic sites, with some methods removing as much as 60% of array probes from consideration before data analysis.ResultsHere, we present an algorithm implemented in an R Bioconductor package, MethylToSNP, which detects a characteristic data pattern to infer sites likely to be confounded by polymorphisms. Additionally, the tool provides a stringent reliability score to allow thresholding on SNP predictions. We calibrated parameters and thresholds used by the algorithm on simulated and real methylation data sets. We illustrate findings using methylation data from YRI (Yoruba in Ibadan, Nigeria), CEPH (European descent) and KhoeSan (southern African) populations. Our polymorphism predictions made using MethylToSNP have been validated through SNP databases and bisulfite and genomic sequencing.ConclusionsThe benefits of this method are threefold. First, it prevents extensive data loss by considering only SNPs specific to the individuals in the study. Second, it offers the possibility to identify new polymorphisms in samples for which there is little known about the genetic landscape. Third, it identifies variants as they exist in functional regions of a genome, such as in CTCF (transcriptional repressor) sites and enhancers, that may be common alleles or personal mutations with potential to deleteriously affect genomic regulatory activities. We demonstrate that MethylToSNP is applicable to the Illumina 450K and Illumina 850K EPIC array data and is also backwards compatible to the 27K methylation arrays. Going forward, this kind of nuanced approach can increase the amount of information derived from precious data sets by considering samples of the project individually to enable more informed decisions about data cleaning.

Dataset Information

Variation in DNA methylation of human blood over a 1-year period using the Illumina MethylationEPIC array.

Publications

Variation in DNA methylation of human blood over a 1-year period using the Illumina MethylationEPIC array.

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